For marketing in stores and the like, the age brackets of customers coming to the store are analyzed and the type and quantity of merchandises are managed on the basis of the analysis.
For example, in convenience stores and the like, employed is the method of analyzing the age brackets of customers by estimating the age groups of the customers by clerks at the time of checkout and inputting the estimation result.
However, the result of manual age estimation impairs objectivity because it greatly includes the personal point of view of an estimator. Therefore, there is a demand for mechanical estimation of a human age with an identification device or the like on the basis of facial image data.
The method of mechanically estimating a human age with an identification device or the like on the basis of facial image data can be broadly divided into two methods. One is the method of identifying age categories such as child, adult, and senior, which is the method of estimating an age as a discrete quantity (the method in which age estimation is performed as an identification problem). The other is the method of identifying an age itself, which is the method of estimating an age as a continuous quantity (the method in which age estimation is performed as a regression problem).
In the method of identifying age categories as in the invention disclosed in Patent Document 1, for example, it is also attempted to identify age categories by subdividing on 10 ages basis. However, in the case where an age problem is solved as an identification problem, there are following problems:    data desired to be kept away from each other are brought closer to each other whereas data desired to be close to each other are separated from each other; and    the relationship of continuous ages cannot be expressed.    These problems cause the decrease in accuracy of age recognition.
For example, in the case where the categories such as the age of 10 to 19 and the age of 20 to 29 are provided at the time of identifying age categories on 10 ages basis, the contradiction arises that ages having 1 year difference therebetween such as 19 years old and 20 years old are intended to be separated from each other and ages having 9 years difference therebetween such as 10 years old and 19 years old are intended to be brought closer.
Further, when one category is separated away from other categories, the distance among the categories cannot be changed. For example, when the category of 10s is separated away from all other age groups, the distance between 10s and 20s is the same as that between 10s and 50s. That is, the distance between distant age groups cannot be extended and the distance between near age groups cannot be reduced.
In contrast, in the case where age estimation is performed as a regression problem as in Non-Patent Document 1, since continuous ages can be expressed, it has been demonstrated by experiments that the contradiction less arises as compared to the case where age estimation is performed as an identification problem and an age can be recognized with high accuracy.
The regression problem can be obtained by solving the difference between an estimated age and a correct age as a problem of minimization. Specific examples include the multiple linear regression analysis and the (kernel) ridge regression. These methods execute learning so as to reduce the mean square error or the mean absolute error between the estimated age and the correct age.
FIG. 9 shows an example of the age estimation apparatus that performs age estimation as a regression problem. Generally, the image data to be inputted is high dimensional data such as the number of pixels or the number of pixels×3 (color values of R, G, and B). Therefore, in a dimension compressor 61, features are extracted from image data such that age information is emphasized and unnecessary information (lighting condition, facial angle, and the like) is deleted. For example, methods such as the principal component analysis (PCA), the linear discriminant analysis (LDA), and the locality preserving projection (LPP) are employed. This processing is also referred to as the “feature selection” and “dimension compression”.
Then, an identification device 62 estimates an age on the basis of the features extracted.
In order to estimate an age with an age estimation apparatus 60 on the basis of the image data, learning of the dimension compressor 61 and the identification device 62 is required.
That is, plural image data of people whose correct ages (actual ages or perceptual ages) are known are inputted to the dimension compressor 61, and each data is evaluated by the methods such as the N-fold cross validation and the leave-one-out cross-validation. On the basis of this evaluation result, an output of the identification device 62 is adjusted so that the error (the difference between the estimated age and the correct age) would be reduced. For the learning of the identification device 62, the methods such as the linear regression, the multiple regression, the ridge regression, and the neural network are applied.
By repeating similar procedures while changing the type and combination of features, the extraction method (i.e., a parameter used for dimension compression), and the like, a parameter and a model are selected so that the error would be reduced.